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Understanding User-Community Engagement by Multi-faceted Features: A Case Study on Twitter,[object Object],March 29, 2011,[object Object],SoME 2011 (In Conjunction with WWW 2011),[object Object],Hemant Purohit1, Yiye Ruan2, Amruta Joshi2,,[object Object],Srinivasan Parthasarthy2, Amit Sheth1,[object Object],1Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Wright State University, USA,[object Object],2Dept. of Computer Science & Engineering,[object Object],Ohio State University, USA,[object Object]
Outline,[object Object],(What) User-Community Engagement,[object Object],(Why) Motivation,[object Object],(How) Problem Formalization,[object Object],Approach,[object Object],Terminology,[object Object],Definition,[object Object],Analysis Framework,[object Object],People-Content-Network Analysis (PCNA),[object Object],Experiments ,[object Object],Datasets and Event Categorization,[object Object],Features,[object Object],Results,[object Object],Insights,[object Object],Conclusion & Future work,[object Object],2,[object Object]
User-communityEngagement,[object Object],Multiple topics surrounding events being discussed on social media,[object Object],Each topic constitutes a community of users discussing about it,[object Object],e.g., Japan Earthquake community,[object Object],How do we understand  the phenomenon of user participation (engagement) in topic discussions,[object Object],3,[object Object],Image: http://itcilo.wordpress.com,[object Object]
Motivation,[object Object],User Engagement Analysis,[object Object],Business,[object Object],How communities form during the product launch?,[object Object],What factors can attract users to engage in these communities, therefore further spreading the message?,[object Object],Crisis Management,[object Object],Effective communication: How quickly we can disseminate information between resource providers and people in need of resources?,[object Object],4,[object Object]
Problem formalization: Approach,[object Object],User engagement has been studied in many forms:,[object Object], Community Formation & Detection, Information Propagation, Link Prediction etc.,[object Object],It involves a three-dimensional dynamic at play: ,[object Object],Content: topic of interest, ,[object Object],People: participants who engages in discussion about the topic, and,[object Object],Network: community structure formed around the topic discussion,[object Object],Rather than limiting to one dimension, we propose multidimensional approach,[object Object],Case Study on Twitter,[object Object],5,[object Object]
Earlier Approaches,[object Object],6,[object Object],Content: Topic of Interest,[object Object],OR,[object Object],People: Participant of the discussion,[object Object],OR,[object Object],Network: Community  around topic,[object Object],Images: tupper-lake.com/.../uploads/Community.jpg,[object Object],                http://www.iconarchive.com/show/people-icons-by-aha-soft/user-icon.html,[object Object]
Our Approach,[object Object],7,[object Object],Content: Topic of Interest,[object Object],AND,[object Object],People: Participant of the discussion,[object Object],AND,[object Object],Network: Community  around topic,[object Object],Images: tupper-lake.com/.../uploads/Community.jpg,[object Object],                http://www.iconarchive.com/show/people-icons-by-aha-soft/user-icon.html,[object Object]
Problem formalization: Terminology,[object Object],Event-Oriented Community,[object Object],An implicit group of social network users who have joined discussion (by message posting) on topic about an event.,[object Object],Slice,[object Object],Collection of messages relevant to topic of discussion, posted during a fixed-length time window.,[object Object],Snapshot,[object Object],State of the network at a certain point of time at which user profile and connection information are crawled.,[object Object],Active Window: freshness matters!,[object Object],Active Community,[object Object],8,[object Object]
Problem formalization: Definition,[object Object],Binary classification problem for user-community link prediction.,[object Object],User Engagement Prediction Problem: Given ,[object Object],1) an event-oriented community C formed around a topic of discussion; ,[object Object],2) a Twitter user U ε C, ,[object Object],Predict whether U will be engaged in C (by composing a new tweet or retweeting an existing tweet which contains keywords or hashtag related to C's underlying event) in a future slice. If so, U is said to be a positive record. Otherwise, it is a negative record.,[object Object],9,[object Object]
Analysis Framework:People-Content-network Analysis (PCNA),[object Object],10,[object Object]
Experiments: Dataset & Event categorization,[object Object],Study on Twitter data,[object Object],Events have various characteristics and we hypothesize the user engagement analysis for them being affected by different variables,[object Object],No standard event categorization is available, so we categorize the events observing data over a time, as follows:,[object Object],Global (G) vs. Local (L)[e.g., Japan Earthquake vs. Iowa State Fair],[object Object],Deterministic (D) vs. Unexpected (U)[e.g., Emmy Awards vs. Japan Earthquake],[object Object],Compact (C) vs. Loose (Ls)[e.g., ISWC conference vs. Japan Earthquake],[object Object],Transient (T) vs. Lasting (Lt)[e.g., President’s Speech vs. Egypt Revolution],[object Object],11,[object Object]
ClevelandShowPremiere: Second Season premiere of animated TV series Cleveland Show. September 26. Global, loose, deterministic, transient.,[object Object],DiscoveryBuildingCrisis: Hostage crisis at the head- quarters of Discovery Channel, Maryland. September 1. Local, loose, unexpected, transient.,[object Object],EmmyAwards: 62nd Prime-time Emmy Awards. August 29. Global, loose, deterministic, lasting.,[object Object],GoogleInstantSearch: Launch of Google Instant in United States. September 8. Global, loose, unexpected, transient.,[object Object],HeismanTrophy: Reggie Bush’s announcement to forfeit 2005 Heisman Trophy. September 14. Local, compact, unexpected, lasting.,[object Object],IowaStateFair: Iowa State Fair. August 12-22. Local, loose, deterministic, lasting.,[object Object],JewishNewYear: Jewish New Year 5771. September 8-10. Global, compact, deterministic, transient.,[object Object],LindsayLohanHearing: LindsayLohan’s hearing on probation revocation and verdict. September 24. Local, loose, deterministic, transient.,[object Object],LinuxCon: Annual convention organized by Linux Foundation. August 10-12. Global, compact, deterministic, lasting.,[object Object],LondonTubeStrike: London tube strike. September 6. Local, loose, deterministic, transient.,[object Object],RichCroninDeath: Death of singer and songwriter Rich Cronin. September 8. Local, loose, unexpected, transient.,[object Object],ScottPilgrimRelease: Release of movie Scott Pilgrim vs. the World. Aug 13. Global, loose, deterministic, lasting.,[object Object],SESSanFrancisco: Search Engine Strategies 2010 at San Francisco. August 16-20. Global, compact, deterministic, lasting.,[object Object],StuxnetWorm: Confirmation of Stuxnet worm at- tack on Iranian nuclear program. September 24. Global, loose, unexpected, lasting.,[object Object],12,[object Object],Events  (labeled as per our categorization),[object Object]
Experiments: Features,[object Object],Organized in the PCNA framework: Node/Author features (P), Content features (C), Community features (N),[object Object],Extracted for each potential community member (U) in each slice, where U belongs to the union of follower lists of each active community member,[object Object],13,[object Object],Followee of (U),[object Object],Whole Topic Community,[object Object],Potential new Member (U),[object Object],EDGE:,[object Object],Active Community,[object Object],A,[object Object],B,[object Object],If B follows A,[object Object]
Experiments: Features (cont.),[object Object],[object Object],wccSize: size of the weakly-connected component (WCC) which U’s friends belongs to in the active network.,[object Object],wccPercent: ratio of wccSizeto the size of the active network.,[object Object],connectivity: number of active friends (i.e. followees) in the community.,[object Object],communitySize: size of the active community.,[object Object],Author features [Characteristics of friends that U is following]:,[object Object],Only friends in the active community are considered.,[object Object],logFollower: logarithm of follower count,[object Object],logFollowee: logarithm of followee count,[object Object],Klout[1]: a integrated measure of user influence and popularity,[object Object],Other profile information and activity history[2].,[object Object],[1] http://www.klout.com,[object Object],[2] Future works,[object Object],14,[object Object]
Experiments: Features (cont.),[object Object],Content features[Characteristics of tweets posted by active friends of U]:,[object Object],keywords: number of event-relevant keywords,[object Object],hashtags: number of event-relevant hashtags,[object Object],retweet: number of retweets,[object Object],mention: number of mentions,[object Object],url: number of relevancy-adjust hyperlinks,[object Object],Irrelevant hyperlink is given number -1,[object Object],subjectivity: Subjectivity scores for words and emoticons,[object Object],Linguistic Cues (LIWC1 analysis): Features for the language usage. Top-3 transformed features using Principle Component Analysis (PCA) extracted,[object Object],15,[object Object],1http://www.liwc.net,[object Object]
Wait a minute! ,[object Object],Not all contents have been viewed!,[object Object],Novelty and Attention: User is likely to see new or recent content/tweet and then join the community,[object Object],Apply temporal weighting on the features ,[object Object],Dataset imbalance: too many negative records!,[object Object],Alleviated by SMOTE method,[object Object],Over-sampling on positive records and under-sampling on negative ones,[object Object],Not all users are active!,[object Object],Apply weighting on activity level based on last activity[1],[object Object],16,[object Object],[1] Future works,[object Object]
Experiments,[object Object],We run the following experiment groups:,[object Object],allFeatures (All): contains all three feature groups,[object Object],onlyContent (Con.): contains only content feature,[object Object],onlyAuthor (Aut.): contains only author feature,[object Object],onlyCommunity (Com.): contains only community feature,[object Object],SVM classifier,[object Object],LibSVM, RBF Kernel, gamma=8, c=32,[object Object],17,[object Object]
Experiments: Results,[object Object],18,[object Object],Event-Type,[object Object],Summary of Prediction Accuracy (%),[object Object],Statistical significant results are in bold,[object Object]
Insights,[object Object],Performance of onlyCommunity classifiers is worst,[object Object],The latent nature of network features makes it difficult to be perceived by a user directly.,[object Object],The onlyContent classifiers give the best performance over other single feature groups,[object Object],Some users end up participating in a discussion based on observing the information from the public timeline, and therefore, these ad-hoc users are hard to observe via network analysis only.,[object Object],Content is engaging by its quality and nature (information sharing or call for an action or crowd sourcing). For example, link to an image or video (an evidential content) about Reggie Bush's surrender of Heisman Trophy in September, 2010 is likely to provoke lot more thoughts in a user's mind to engage in the discussion.,[object Object],19,[object Object]
Insights (Cont.),[object Object],Comparable performance of onlyAuthor classifiers as onlyContent classifiers for some of the topics,[object Object],Impact of the effective presence of influential people in the discussion group,[object Object],Insufficiency in content features, reflected by low average connectivity, can be compensated by author features (e.g., Rich Cronin Death).,[object Object],Statistical significance testing method shows allFeatures classifiers have better or equivalent performance over any single feature group classifier for 12 out of 14 topics,[object Object],The advantage of using all features is dominant, where degree of randomness in individual dimensions can be really high (e.g., Discovery Building attack).,[object Object],20,[object Object]
Insights (Cont.),[object Object],No significant correlation between selection of feature groups and the event types: lasting vs. transient. ,[object Object],Possibility of the shift in the characteristics over time,[object Object],Advantage of allFeatures over other factor groups is generally stronger on the unexpected topics than the deterministic ones.,[object Object],Degree of randomness being high in discussions surrounding unexpected events,[object Object],21,[object Object]
Conclusion & Future Work,[object Object],Every dimension (People, Content, Network) cannot be expected to perform well in all types of topic discussions, and hence, a strong need can be felt to study dynamics of user engagement by using the PCNA framework.,[object Object],Experiments with a more refined event types taxonomy and user engagement factors, with consideration of shift in the event characteristics over time,[object Object],Semantic Analysis of content to enhance content features,[object Object],Experiment on other social networks: Forums, DBLP,[object Object],22,[object Object]
Questions?,[object Object],Paper at: http://knoesis.org/library/resource.php?id=1095,[object Object],More on Social Media @ Kno.e.sis at http://knoesis.org/research/semweb/projects/socialmedia/,[object Object],23,[object Object]

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Understanding User-Community Engagement by Multi-faceted Features: A Case Study on Twitter

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Hinweis der Redaktion

  1. Active window: due to concept of ‘novelty and attention’ in today’s social media systems’ design ---- (reason is information overload for a user to keep up with!!)
  2. Observe the data of active window Find the followers base of the active community members as the samples for the prediction problem Analyze features for them, and classify, whether the follower U is going to join the community C
  3. Global (G) vs. Local (L) – Scale (how many people will it attract)[e.g., Japan Earthquake vs. Iowa State Fair]Deterministic (D) vs. Unexpected (U) -- (expected)[e.g., Emmy Awards vs. Japan Earthquake]Compact (C) vs. Loose (Ls) -- (If people already know each other, so they are connected tightly)[e.g., ISWC conference vs. Japan Earthquake]Transient (T) vs. Lasting (Lt) -- (Event lasts for just a small time vs. long time)[e.g., President’s Speech vs. Egypt Revolution]
  4. Essentially 3 major types for features:For Community surrounding the event size growth rateFor usersUsers that you follow, mention or retweet fromUsers that share a large overlap of friends with youUsers that share similar profilesFor tweet content[only from ‘followees’ of new users joining the community]url, mentions, RT, # Linguistics analysis features
  5. Using style of writing for authorhashtags usage affects -- attention